Overview

Dataset statistics

Number of variables43
Number of observations28953
Missing cells501215
Missing cells (%)40.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 MiB
Average record size in memory344.0 B

Variable types

Unsupported5
Categorical20
Numeric18

Alerts

CEOANN has constant value ""Constant
PCEO has constant value ""Constant
PCFO has constant value ""Constant
CONAME has a high cardinality: 386 distinct valuesHigh cardinality
COMMENT has a high cardinality: 1413 distinct valuesHigh cardinality
BECAMECEO has a high cardinality: 559 distinct valuesHigh cardinality
JOINED_CO has a high cardinality: 564 distinct valuesHigh cardinality
LEFTOFC has a high cardinality: 659 distinct valuesHigh cardinality
LEFTCO has a high cardinality: 780 distinct valuesHigh cardinality
TITLE has a high cardinality: 2655 distinct valuesHigh cardinality
EXEC_LNAME has a high cardinality: 3976 distinct valuesHigh cardinality
EXEC_FNAME has a high cardinality: 811 distinct valuesHigh cardinality
CUSIP has a high cardinality: 386 distinct valuesHigh cardinality
TICKER has a high cardinality: 386 distinct valuesHigh cardinality
REPRICE is highly imbalanced (96.2%)Imbalance
GENDER is highly imbalanced (76.1%)Imbalance
CFOANN has 28953 (100.0%) missing valuesMissing
EXECRANK has 28747 (99.3%) missing valuesMissing
CEOANN has 24685 (85.3%) missing valuesMissing
PENSION_CHG has 28953 (100.0%) missing valuesMissing
TOTAL_SEC has 28953 (100.0%) missing valuesMissing
CHG_CTRL_PYMT has 28953 (100.0%) missing valuesMissing
AGE has 18708 (64.6%) missing valuesMissing
EXECRANKANN has 7094 (24.5%) missing valuesMissing
TDC1 has 4801 (16.6%) missing valuesMissing
SAL_PCT has 5288 (18.3%) missing valuesMissing
TOTAL_CURR_PCT has 5283 (18.2%) missing valuesMissing
TOTAL_SEC_PCT has 28953 (100.0%) missing valuesMissing
TDC1_PCT has 10584 (36.6%) missing valuesMissing
COMMENT has 26778 (92.5%) missing valuesMissing
BECAMECEO has 20979 (72.5%) missing valuesMissing
JOINED_CO has 19336 (66.8%) missing valuesMissing
REJOIN has 28787 (99.4%) missing valuesMissing
LEFTOFC has 21623 (74.7%) missing valuesMissing
LEFTCO has 20082 (69.4%) missing valuesMissing
RELEFT has 28883 (99.8%) missing valuesMissing
PCEO has 28285 (97.7%) missing valuesMissing
PCFO has 28858 (99.7%) missing valuesMissing
REASON has 20141 (69.6%) missing valuesMissing
PAGE has 7491 (25.9%) missing valuesMissing
TDC1 is highly skewed (γ1 = 26.79319677)Skewed
ALLOTHTOT is highly skewed (γ1 = 45.88204098)Skewed
ALLOTHPD is highly skewed (γ1 = 61.41823403)Skewed
SAL_PCT is highly skewed (γ1 = 64.95114336)Skewed
TOTAL_CURR_PCT is highly skewed (γ1 = 39.72659118)Skewed
TDC1_PCT is highly skewed (γ1 = 64.95675232)Skewed
CFOANN is an unsupported type, check if it needs cleaning or further analysisUnsupported
PENSION_CHG is an unsupported type, check if it needs cleaning or further analysisUnsupported
TOTAL_SEC is an unsupported type, check if it needs cleaning or further analysisUnsupported
CHG_CTRL_PYMT is an unsupported type, check if it needs cleaning or further analysisUnsupported
TOTAL_SEC_PCT is an unsupported type, check if it needs cleaning or further analysisUnsupported
BONUS has 2849 (9.8%) zerosZeros
ALLOTHTOT has 2466 (8.5%) zerosZeros
ALLOTHPD has 26385 (91.1%) zerosZeros
SAL_PCT has 2347 (8.1%) zerosZeros
TOTAL_CURR_PCT has 424 (1.5%) zerosZeros

Reproduction

Analysis started2023-04-27 19:12:41.593834
Analysis finished2023-04-27 19:13:39.287706
Duration57.69 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

CFOANN
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing28953
Missing (%)100.0%
Memory size226.3 KiB

EXECDIR
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size226.3 KiB
0
19666 
1
9287 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28953
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19666
67.9%
1 9287
32.1%

Length

2023-04-27T15:13:39.480635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T15:13:39.608511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19666
67.9%
1 9287
32.1%

Most occurring characters

ValueCountFrequency (%)
0 19666
67.9%
1 9287
32.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28953
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19666
67.9%
1 9287
32.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28953
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19666
67.9%
1 9287
32.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19666
67.9%
1 9287
32.1%

REPRICE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size226.3 KiB
0
28836 
1
 
117

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28953
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28836
99.6%
1 117
 
0.4%

Length

2023-04-27T15:13:39.741213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T15:13:39.897542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28836
99.6%
1 117
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 28836
99.6%
1 117
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28953
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28836
99.6%
1 117
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 28953
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28836
99.6%
1 117
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28836
99.6%
1 117
 
0.4%

EXECRANK
Real number (ℝ)

Distinct7
Distinct (%)3.4%
Missing28747
Missing (%)99.3%
Infinite0
Infinite (%)0.0%
Mean2.4368932
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size226.3 KiB
2023-04-27T15:13:40.008810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7372246
Coefficient of variation (CV)0.712885
Kurtosis-0.16554224
Mean2.4368932
Median Absolute Deviation (MAD)1
Skewness1.0202751
Sum502
Variance3.0179493
MonotonicityNot monotonic
2023-04-27T15:13:40.137837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 93
 
0.3%
2 38
 
0.1%
3 24
 
0.1%
5 17
 
0.1%
4 16
 
0.1%
6 14
 
< 0.1%
7 4
 
< 0.1%
(Missing) 28747
99.3%
ValueCountFrequency (%)
1 93
0.3%
2 38
0.1%
3 24
 
0.1%
4 16
 
0.1%
5 17
 
0.1%
6 14
 
< 0.1%
7 4
 
< 0.1%
ValueCountFrequency (%)
7 4
 
< 0.1%
6 14
 
< 0.1%
5 17
 
0.1%
4 16
 
0.1%
3 24
 
0.1%
2 38
0.1%
1 93
0.3%

CO_PER_ROL
Real number (ℝ)

Distinct5173
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13451.388
Minimum1
Maximum36815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size226.3 KiB
2023-04-27T15:13:40.280990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile702
Q13914
median13342
Q321541
95-th percentile28917
Maximum36815
Range36814
Interquartile range (IQR)17627

Descriptive statistics

Standard deviation9627.1892
Coefficient of variation (CV)0.71570228
Kurtosis-1.2503652
Mean13451.388
Median Absolute Deviation (MAD)8980
Skewness0.18137482
Sum3.8945804 × 108
Variance92682772
MonotonicityNot monotonic
2023-04-27T15:13:40.433941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2593 14
 
< 0.1%
7995 14
 
< 0.1%
2865 14
 
< 0.1%
3816 14
 
< 0.1%
3817 14
 
< 0.1%
6562 14
 
< 0.1%
6564 14
 
< 0.1%
2816 14
 
< 0.1%
3861 14
 
< 0.1%
7996 14
 
< 0.1%
Other values (5163) 28813
99.5%
ValueCountFrequency (%)
1 7
< 0.1%
2 10
< 0.1%
3 12
< 0.1%
4 11
< 0.1%
5 3
 
< 0.1%
6 7
< 0.1%
7 7
< 0.1%
8 9
< 0.1%
9 3
 
< 0.1%
10 6
< 0.1%
ValueCountFrequency (%)
36815 2
 
< 0.1%
36813 1
 
< 0.1%
36804 2
 
< 0.1%
36803 2
 
< 0.1%
36802 5
< 0.1%
36730 3
< 0.1%
36729 4
< 0.1%
36728 4
< 0.1%
36188 5
< 0.1%
36186 2
 
< 0.1%

CONAME
Categorical

Distinct386
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size226.3 KiB
PG&E CORP
 
138
EDISON INTERNATIONAL
 
124
SCHWAB (CHARLES) CORP
 
118
CONOCOPHILLIPS
 
112
FORD MOTOR CO
 
111
Other values (381)
28350 

Length

Max length28
Median length21
Mean length16.821228
Min length5

Characters and Unicode

Total characters487025
Distinct characters36
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAMERICAN AIRLINES GROUP INC
2nd rowAMERICAN AIRLINES GROUP INC
3rd rowAMERICAN AIRLINES GROUP INC
4th rowAMERICAN AIRLINES GROUP INC
5th rowAMERICAN AIRLINES GROUP INC

Common Values

ValueCountFrequency (%)
PG&E CORP 138
 
0.5%
EDISON INTERNATIONAL 124
 
0.4%
SCHWAB (CHARLES) CORP 118
 
0.4%
CONOCOPHILLIPS 112
 
0.4%
FORD MOTOR CO 111
 
0.4%
CMS ENERGY CORP 109
 
0.4%
MICRON TECHNOLOGY INC 108
 
0.4%
AMERICAN INTERNATIONAL GROUP 107
 
0.4%
EXELON CORP 106
 
0.4%
WILLIAMS COS INC 106
 
0.4%
Other values (376) 27814
96.1%

Length

2023-04-27T15:13:40.590726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 12909
 
16.0%
corp 7891
 
9.8%
co 3839
 
4.7%
1734
 
2.1%
energy 1348
 
1.7%
group 1311
 
1.6%
plc 865
 
1.1%
financial 728
 
0.9%
intl 685
 
0.8%
technologies 675
 
0.8%
Other values (546) 48917
60.5%

Most occurring characters

ValueCountFrequency (%)
52222
10.7%
C 44637
 
9.2%
N 41102
 
8.4%
O 39829
 
8.2%
E 37988
 
7.8%
I 36702
 
7.5%
R 36678
 
7.5%
A 29244
 
6.0%
S 22407
 
4.6%
L 22142
 
4.5%
Other values (26) 124074
25.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 428145
87.9%
Space Separator 52222
 
10.7%
Other Punctuation 2710
 
0.6%
Dash Punctuation 1435
 
0.3%
Open Punctuation 1150
 
0.2%
Close Punctuation 1150
 
0.2%
Decimal Number 213
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 44637
10.4%
N 41102
9.6%
O 39829
9.3%
E 37988
 
8.9%
I 36702
 
8.6%
R 36678
 
8.6%
A 29244
 
6.8%
S 22407
 
5.2%
L 22142
 
5.2%
T 21915
 
5.1%
Other values (16) 95501
22.3%
Other Punctuation
ValueCountFrequency (%)
& 2053
75.8%
' 290
 
10.7%
. 267
 
9.9%
/ 100
 
3.7%
Decimal Number
ValueCountFrequency (%)
3 191
89.7%
5 22
 
10.3%
Space Separator
ValueCountFrequency (%)
52222
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1435
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1150
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 428145
87.9%
Common 58880
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 44637
10.4%
N 41102
9.6%
O 39829
9.3%
E 37988
 
8.9%
I 36702
 
8.6%
R 36678
 
8.6%
A 29244
 
6.8%
S 22407
 
5.2%
L 22142
 
5.2%
T 21915
 
5.1%
Other values (16) 95501
22.3%
Common
ValueCountFrequency (%)
52222
88.7%
& 2053
 
3.5%
- 1435
 
2.4%
( 1150
 
2.0%
) 1150
 
2.0%
' 290
 
0.5%
. 267
 
0.5%
3 191
 
0.3%
/ 100
 
0.2%
5 22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 487025
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
52222
10.7%
C 44637
 
9.2%
N 41102
 
8.4%
O 39829
 
8.2%
E 37988
 
7.8%
I 36702
 
7.5%
R 36678
 
7.5%
A 29244
 
6.0%
S 22407
 
4.6%
L 22142
 
4.5%
Other values (26) 124074
25.5%

CEOANN
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing24685
Missing (%)85.3%
Memory size226.3 KiB
CEO
4268 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12804
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCEO
2nd rowCEO
3rd rowCEO
4th rowCEO
5th rowCEO

Common Values

ValueCountFrequency (%)
CEO 4268
 
14.7%
(Missing) 24685
85.3%

Length

2023-04-27T15:13:40.704751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T15:13:40.822604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ceo 4268
100.0%

Most occurring characters

ValueCountFrequency (%)
C 4268
33.3%
E 4268
33.3%
O 4268
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12804
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 4268
33.3%
E 4268
33.3%
O 4268
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 12804
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 4268
33.3%
E 4268
33.3%
O 4268
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 4268
33.3%
E 4268
33.3%
O 4268
33.3%

SALARY
Real number (ℝ)

Distinct14776
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451.39446
Minimum-0.0001
Maximum6765
Zeros66
Zeros (%)0.2%
Negative1
Negative (%)< 0.1%
Memory size226.3 KiB
2023-04-27T15:13:41.026800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0001
5-th percentile143.3966
Q1255.221
median375
Q3553
95-th percentile1000
Maximum6765
Range6765.0001
Interquartile range (IQR)297.779

Descriptive statistics

Standard deviation314.85195
Coefficient of variation (CV)0.69750956
Kurtosis33.500736
Mean451.39446
Median Absolute Deviation (MAD)137.5
Skewness3.6453475
Sum13069224
Variance99131.752
MonotonicityNot monotonic
2023-04-27T15:13:41.245713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 385
 
1.3%
400 318
 
1.1%
1000 302
 
1.0%
500 293
 
1.0%
600 263
 
0.9%
450 244
 
0.8%
350 223
 
0.8%
250 207
 
0.7%
200 189
 
0.7%
800 152
 
0.5%
Other values (14766) 26377
91.1%
ValueCountFrequency (%)
-0.0001 1
 
< 0.1%
0 66
0.2%
0.001 30
0.1%
0.827 1
 
< 0.1%
1.923 1
 
< 0.1%
2.02 1
 
< 0.1%
2.223 2
 
< 0.1%
2.363 1
 
< 0.1%
2.418 1
 
< 0.1%
4.615 1
 
< 0.1%
ValueCountFrequency (%)
6765 1
< 0.1%
5806.651 1
< 0.1%
5773.077 1
< 0.1%
5306.651 1
< 0.1%
5294.095 1
< 0.1%
4973.073 1
< 0.1%
4600 1
< 0.1%
4476.101 1
< 0.1%
4395.697 1
< 0.1%
4309.092 1
< 0.1%

BONUS
Real number (ℝ)

Distinct14343
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean559.16719
Minimum0
Maximum43511.534
Zeros2849
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size226.3 KiB
2023-04-27T15:13:41.609585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1111.665
median260.825
Q3557.376
95-th percentile1978.92
Maximum43511.534
Range43511.534
Interquartile range (IQR)445.711

Descriptive statistics

Standard deviation1195.9494
Coefficient of variation (CV)2.1388047
Kurtosis164.18055
Mean559.16719
Median Absolute Deviation (MAD)189.175
Skewness9.5003975
Sum16189568
Variance1430295
MonotonicityNot monotonic
2023-04-27T15:13:41.780461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2849
 
9.8%
300 272
 
0.9%
500 229
 
0.8%
200 225
 
0.8%
250 205
 
0.7%
100 204
 
0.7%
400 203
 
0.7%
350 196
 
0.7%
150 193
 
0.7%
600 161
 
0.6%
Other values (14333) 24216
83.6%
ValueCountFrequency (%)
0 2849
9.8%
0.026 1
 
< 0.1%
0.2 1
 
< 0.1%
0.25 2
 
< 0.1%
0.3 2
 
< 0.1%
0.4 1
 
< 0.1%
0.428 1
 
< 0.1%
0.5 1
 
< 0.1%
0.7 11
 
< 0.1%
0.759 1
 
< 0.1%
ValueCountFrequency (%)
43511.534 1
< 0.1%
33000 1
< 0.1%
29000 1
< 0.1%
26640.502 1
< 0.1%
26000 1
< 0.1%
23618.156 1
< 0.1%
22900.821 1
< 0.1%
22000 1
< 0.1%
21519.6 1
< 0.1%
20272.096 1
< 0.1%

PENSION_CHG
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing28953
Missing (%)100.0%
Memory size226.3 KiB

TOTAL_SEC
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing28953
Missing (%)100.0%
Memory size226.3 KiB

TOTAL_CURR
Real number (ℝ)

Distinct22315
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1010.5616
Minimum-0.0001
Maximum43511.535
Zeros52
Zeros (%)0.2%
Negative1
Negative (%)< 0.1%
Memory size226.3 KiB
2023-04-27T15:13:41.935015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0001
5-th percentile203.0792
Q1407.394
median653.533
Q31109.225
95-th percentile2845.833
Maximum43511.535
Range43511.535
Interquartile range (IQR)701.831

Descriptive statistics

Standard deviation1361.38
Coefficient of variation (CV)1.3471519
Kurtosis110.62735
Mean1010.5616
Median Absolute Deviation (MAD)302.7
Skewness7.6558172
Sum29258791
Variance1853355.6
MonotonicityNot monotonic
2023-04-27T15:13:42.091954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 71
 
0.2%
1000 66
 
0.2%
550 66
 
0.2%
800 66
 
0.2%
600 64
 
0.2%
450 64
 
0.2%
750 62
 
0.2%
700 62
 
0.2%
0 52
 
0.2%
400 48
 
0.2%
Other values (22305) 28332
97.9%
ValueCountFrequency (%)
-0.0001 1
 
< 0.1%
0 52
0.2%
0.001 19
 
0.1%
1.631 2
 
< 0.1%
1.724 1
 
< 0.1%
2.223 2
 
< 0.1%
2.363 1
 
< 0.1%
2.418 1
 
< 0.1%
4.615 1
 
< 0.1%
6.231 1
 
< 0.1%
ValueCountFrequency (%)
43511.535 1
< 0.1%
34000 1
< 0.1%
30000 1
< 0.1%
27998.838 1
< 0.1%
27000 1
< 0.1%
23957.5 1
< 0.1%
23550.821 1
< 0.1%
22519.6 1
< 0.1%
22450.137 1
< 0.1%
21906 1
< 0.1%

CHG_CTRL_PYMT
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing28953
Missing (%)100.0%
Memory size226.3 KiB

AGE
Real number (ℝ)

Distinct60
Distinct (%)0.6%
Missing18708
Missing (%)64.6%
Infinite0
Infinite (%)0.0%
Mean54.363202
Minimum28
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size226.3 KiB
2023-04-27T15:13:42.485612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile42
Q149
median55
Q359
95-th percentile66
Maximum87
Range59
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.7443882
Coefficient of variation (CV)0.14245644
Kurtosis0.55546124
Mean54.363202
Median Absolute Deviation (MAD)5
Skewness0.15507555
Sum556951
Variance59.975549
MonotonicityNot monotonic
2023-04-27T15:13:42.628634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 544
 
1.9%
54 543
 
1.9%
55 538
 
1.9%
58 535
 
1.8%
57 532
 
1.8%
52 511
 
1.8%
59 506
 
1.7%
53 501
 
1.7%
51 467
 
1.6%
60 457
 
1.6%
Other values (50) 5111
 
17.7%
(Missing) 18708
64.6%
ValueCountFrequency (%)
28 1
 
< 0.1%
29 2
 
< 0.1%
30 2
 
< 0.1%
31 6
 
< 0.1%
32 6
 
< 0.1%
33 7
 
< 0.1%
34 18
0.1%
35 20
0.1%
36 29
0.1%
37 44
0.2%
ValueCountFrequency (%)
87 1
 
< 0.1%
86 2
 
< 0.1%
85 2
 
< 0.1%
84 3
 
< 0.1%
83 5
 
< 0.1%
82 7
< 0.1%
81 12
< 0.1%
80 14
< 0.1%
79 14
< 0.1%
78 15
0.1%

EXECRANKANN
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing7094
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean3.1344069
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size226.3 KiB
2023-04-27T15:13:42.767431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum12
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.564356
Coefficient of variation (CV)0.49909157
Kurtosis-0.70116284
Mean3.1344069
Median Absolute Deviation (MAD)1
Skewness0.25205962
Sum68515
Variance2.4472098
MonotonicityNot monotonic
2023-04-27T15:13:42.908355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 4275
14.8%
2 4249
14.7%
3 4131
14.3%
4 4089
14.1%
5 4019
13.9%
6 860
 
3.0%
7 177
 
0.6%
8 41
 
0.1%
9 13
 
< 0.1%
11 2
 
< 0.1%
Other values (2) 3
 
< 0.1%
(Missing) 7094
24.5%
ValueCountFrequency (%)
1 4275
14.8%
2 4249
14.7%
3 4131
14.3%
4 4089
14.1%
5 4019
13.9%
6 860
 
3.0%
7 177
 
0.6%
8 41
 
0.1%
9 13
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
11 2
 
< 0.1%
10 2
 
< 0.1%
9 13
 
< 0.1%
8 41
 
0.1%
7 177
 
0.6%
6 860
 
3.0%
5 4019
13.9%
4 4089
14.1%
3 4131
14.3%

TDC1
Real number (ℝ)

MISSING  SKEWED 

Distinct23856
Distinct (%)98.8%
Missing4801
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean3348.4942
Minimum-0.0001
Maximum600347.35
Zeros3
Zeros (%)< 0.1%
Negative1
Negative (%)< 0.1%
Memory size226.3 KiB
2023-04-27T15:13:43.044616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0001
5-th percentile311.12595
Q1803.44075
median1599.531
Q33394.6737
95-th percentile11447.112
Maximum600347.35
Range600347.35
Interquartile range (IQR)2591.233

Descriptive statistics

Standard deviation7718.9535
Coefficient of variation (CV)2.3052014
Kurtosis1616.7014
Mean3348.4942
Median Absolute Deviation (MAD)993.442
Skewness26.793197
Sum80872832
Variance59582243
MonotonicityNot monotonic
2023-04-27T15:13:43.195084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001 11
 
< 0.1%
100 8
 
< 0.1%
122.5 6
 
< 0.1%
108.2 5
 
< 0.1%
314 5
 
< 0.1%
300 4
 
< 0.1%
3716.024 4
 
< 0.1%
81.84 4
 
< 0.1%
250 4
 
< 0.1%
200 4
 
< 0.1%
Other values (23846) 24097
83.2%
(Missing) 4801
 
16.6%
ValueCountFrequency (%)
-0.0001 1
 
< 0.1%
0 3
 
< 0.1%
0.001 11
< 0.1%
1.32 1
 
< 0.1%
4.615 1
 
< 0.1%
8.654 1
 
< 0.1%
11.336 1
 
< 0.1%
20 2
 
< 0.1%
20.796 1
 
< 0.1%
21.923 1
 
< 0.1%
ValueCountFrequency (%)
600347.351 1
< 0.1%
230033.652 1
< 0.1%
204489.424 1
< 0.1%
202185.14 1
< 0.1%
176279.53 1
< 0.1%
159309.275 1
< 0.1%
147586.945 1
< 0.1%
140918.266 1
< 0.1%
137669.87 1
< 0.1%
137669.317 1
< 0.1%

ALLOTHTOT
Real number (ℝ)

SKEWED  ZEROS 

Distinct18645
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.0286
Minimum0
Maximum95195.5
Zeros2466
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size226.3 KiB
2023-04-27T15:13:43.359485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median17.364
Q350.864
95-th percentile270.7228
Maximum95195.5
Range95195.5
Interquartile range (IQR)44.864

Descriptive statistics

Standard deviation998.75688
Coefficient of variation (CV)8.7588281
Kurtosis3369.1492
Mean114.0286
Median Absolute Deviation (MAD)14.377
Skewness45.882041
Sum3301470
Variance997515.31
MonotonicityNot monotonic
2023-04-27T15:13:43.509066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2466
 
8.5%
1.5 218
 
0.8%
6 187
 
0.6%
8 148
 
0.5%
4.8 110
 
0.4%
10 88
 
0.3%
9 87
 
0.3%
5 82
 
0.3%
5.1 78
 
0.3%
7.5 76
 
0.3%
Other values (18635) 25413
87.8%
ValueCountFrequency (%)
0 2466
8.5%
0.003 3
 
< 0.1%
0.025 1
 
< 0.1%
0.03 1
 
< 0.1%
0.034 1
 
< 0.1%
0.039 1
 
< 0.1%
0.066 1
 
< 0.1%
0.069 2
 
< 0.1%
0.073 1
 
< 0.1%
0.074 1
 
< 0.1%
ValueCountFrequency (%)
95195.5 1
< 0.1%
48922.949 1
< 0.1%
40484.594 1
< 0.1%
36479.425 1
< 0.1%
33688.97 1
< 0.1%
33684.634 1
< 0.1%
28978.755 1
< 0.1%
22706.15 1
< 0.1%
22006.1 1
< 0.1%
21859.386 1
< 0.1%

ALLOTHPD
Real number (ℝ)

SKEWED  ZEROS 

Distinct2033
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.674424
Minimum0
Maximum95107.134
Zeros26385
Zeros (%)91.1%
Negative0
Negative (%)0.0%
Memory size226.3 KiB
2023-04-27T15:13:43.667306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile27.2476
Maximum95107.134
Range95107.134
Interquartile range (IQR)0

Descriptive statistics

Standard deviation879.16354
Coefficient of variation (CV)17.698515
Kurtosis5444.9111
Mean49.674424
Median Absolute Deviation (MAD)0
Skewness61.418234
Sum1438223.6
Variance772928.52
MonotonicityNot monotonic
2023-04-27T15:13:43.817049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26385
91.1%
6 21
 
0.1%
50 17
 
0.1%
100 16
 
0.1%
26 16
 
0.1%
1 15
 
0.1%
12.5 14
 
< 0.1%
1.5 13
 
< 0.1%
8.4 12
 
< 0.1%
12 12
 
< 0.1%
Other values (2023) 2432
 
8.4%
ValueCountFrequency (%)
0 26385
91.1%
0.05 2
 
< 0.1%
0.066 1
 
< 0.1%
0.1 1
 
< 0.1%
0.115 1
 
< 0.1%
0.128 1
 
< 0.1%
0.137 1
 
< 0.1%
0.15 2
 
< 0.1%
0.153 1
 
< 0.1%
0.18 1
 
< 0.1%
ValueCountFrequency (%)
95107.134 1
< 0.1%
48917.997 1
< 0.1%
40484.594 1
< 0.1%
33688.97 1
< 0.1%
33684.634 1
< 0.1%
28960.132 1
< 0.1%
21859.386 1
< 0.1%
17687.141 1
< 0.1%
17067.987 1
< 0.1%
16448.276 1
< 0.1%

SAL_PCT
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct12653
Distinct (%)53.5%
Missing5288
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean24.274287
Minimum-100
Maximum25897.582
Zeros2347
Zeros (%)8.1%
Negative1250
Negative (%)4.3%
Memory size226.3 KiB
2023-04-27T15:13:43.983984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-0.1848
Q13.392
median6.897
Q314.016
95-th percentile45.8126
Maximum25897.582
Range25997.582
Interquartile range (IQR)10.624

Descriptive statistics

Standard deviation293.56048
Coefficient of variation (CV)12.093475
Kurtosis5313.7345
Mean24.274287
Median Absolute Deviation (MAD)4.862
Skewness64.951143
Sum574451.01
Variance86177.758
MonotonicityNot monotonic
2023-04-27T15:13:44.141352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2347
 
8.1%
5 156
 
0.5%
10 140
 
0.5%
4 107
 
0.4%
11.111 106
 
0.4%
5.263 86
 
0.3%
12.5 75
 
0.3%
7.143 74
 
0.3%
6.667 74
 
0.3%
14.286 71
 
0.2%
Other values (12643) 20429
70.6%
(Missing) 5288
 
18.3%
ValueCountFrequency (%)
-100 11
< 0.1%
-99.999 2
 
< 0.1%
-99.998 2
 
< 0.1%
-98.485 1
 
< 0.1%
-96.217 1
 
< 0.1%
-94.494 1
 
< 0.1%
-93.675 1
 
< 0.1%
-92.334 1
 
< 0.1%
-91.923 1
 
< 0.1%
-91.912 1
 
< 0.1%
ValueCountFrequency (%)
25897.582 1
< 0.1%
25890.099 1
< 0.1%
12650.494 1
< 0.1%
8275.295 1
< 0.1%
7100.461 1
< 0.1%
6736.213 1
< 0.1%
5600 1
< 0.1%
5386.542 1
< 0.1%
4410.703 1
< 0.1%
4098.794 1
< 0.1%

TOTAL_CURR_PCT
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct19458
Distinct (%)82.2%
Missing5283
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean30.972142
Minimum-100
Maximum16361.776
Zeros424
Zeros (%)1.5%
Negative5778
Negative (%)20.0%
Memory size226.3 KiB
2023-04-27T15:13:44.298255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-33.85755
Q10
median11.0595
Q329.39175
95-th percentile103.70755
Maximum16361.776
Range16461.776
Interquartile range (IQR)29.39175

Descriptive statistics

Standard deviation227.17823
Coefficient of variation (CV)7.3349214
Kurtosis2345.8265
Mean30.972142
Median Absolute Deviation (MAD)13.9405
Skewness39.726591
Sum733110.6
Variance51609.947
MonotonicityNot monotonic
2023-04-27T15:13:44.483815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 424
 
1.5%
10 30
 
0.1%
7.143 26
 
0.1%
5 23
 
0.1%
8.333 22
 
0.1%
11.111 20
 
0.1%
16.667 20
 
0.1%
9.091 19
 
0.1%
14.286 19
 
0.1%
20 19
 
0.1%
Other values (19448) 23048
79.6%
(Missing) 5283
 
18.2%
ValueCountFrequency (%)
-100 12
< 0.1%
-99.999 1
 
< 0.1%
-98.907 1
 
< 0.1%
-98.035 1
 
< 0.1%
-96.923 1
 
< 0.1%
-96.4 1
 
< 0.1%
-96.195 1
 
< 0.1%
-95.117 1
 
< 0.1%
-95.037 1
 
< 0.1%
-94.975 1
 
< 0.1%
ValueCountFrequency (%)
16361.776 1
< 0.1%
15888.314 1
< 0.1%
7819.747 1
< 0.1%
6713.052 1
< 0.1%
6017.611 1
< 0.1%
5640.907 1
< 0.1%
5406.47 1
< 0.1%
5047.943 1
< 0.1%
4708.101 1
< 0.1%
4464.789 1
< 0.1%

TOTAL_SEC_PCT
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing28953
Missing (%)100.0%
Memory size226.3 KiB

TDC1_PCT
Real number (ℝ)

MISSING  SKEWED 

Distinct17364
Distinct (%)94.5%
Missing10584
Missing (%)36.6%
Infinite0
Infinite (%)0.0%
Mean57.279147
Minimum-100
Maximum61584.348
Zeros31
Zeros (%)0.1%
Negative6646
Negative (%)23.0%
Memory size226.3 KiB
2023-04-27T15:13:44.661378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-62.4366
Q1-14.179
median12.496
Q349.196
95-th percentile220.7852
Maximum61584.348
Range61684.348
Interquartile range (IQR)63.375

Descriptive statistics

Standard deviation622.8505
Coefficient of variation (CV)10.873949
Kurtosis5628.7641
Mean57.279147
Median Absolute Deviation (MAD)30.634
Skewness64.956752
Sum1052160.7
Variance387942.75
MonotonicityNot monotonic
2023-04-27T15:13:44.813359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
 
0.1%
18.205 5
 
< 0.1%
-100 5
 
< 0.1%
2.491 4
 
< 0.1%
5.765 4
 
< 0.1%
12.607 3
 
< 0.1%
21.379 3
 
< 0.1%
-5.905 3
 
< 0.1%
-32.812 3
 
< 0.1%
19.62 3
 
< 0.1%
Other values (17354) 18305
63.2%
(Missing) 10584
36.6%
ValueCountFrequency (%)
-100 5
< 0.1%
-99.999 1
 
< 0.1%
-99.976 1
 
< 0.1%
-99.888 1
 
< 0.1%
-99.842 1
 
< 0.1%
-99.81 1
 
< 0.1%
-99.088 1
 
< 0.1%
-98.825 1
 
< 0.1%
-98.81 1
 
< 0.1%
-98.62 1
 
< 0.1%
ValueCountFrequency (%)
61584.348 1
< 0.1%
26917.081 1
< 0.1%
26044.747 1
< 0.1%
19328.696 1
< 0.1%
13753.536 1
< 0.1%
12836.159 1
< 0.1%
12066.569 1
< 0.1%
11154.725 1
< 0.1%
8152.02 1
< 0.1%
8138.907 1
< 0.1%

COMMENT
Categorical

HIGH CARDINALITY  MISSING 

Distinct1413
Distinct (%)65.0%
Missing26778
Missing (%)92.5%
Memory size226.3 KiB
*Excl. portion of bonus paid in restricted stock.
 
109
*May contain preliminary information.
 
56
*Excl. 50% of bonus paid in stock options.
 
31
*Excludes portion of bonus paid in restricted stock.
 
26
*Compensation also listed under USX-U.S. Steel.
 
23
Other values (1408)
1930 

Length

Max length235
Median length181
Mean length50.11954
Min length12

Characters and Unicode

Total characters109010
Distinct characters77
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1223 ?
Unique (%)56.2%

Sample

1st row*1994 bonus has not been determined.
2nd row*1996 LTIP has not yet been determined.
3rd row*Includes $638,600 in consulting/severance payments and $73,684 in accrued vacation pay.
4th rowIncludes $1,156,981 in consulting/severance payments and $60,000 in accrued vacation pay.
5th row*Incl one additional pay period.

Common Values

ValueCountFrequency (%)
*Excl. portion of bonus paid in restricted stock. 109
 
0.4%
*May contain preliminary information. 56
 
0.2%
*Excl. 50% of bonus paid in stock options. 31
 
0.1%
*Excludes portion of bonus paid in restricted stock. 26
 
0.1%
*Compensation also listed under USX-U.S. Steel. 23
 
0.1%
*Excl. portion of bonus pd in restricted stock. 21
 
0.1%
*Compensation also listed under Newmont Gold Co. 14
 
< 0.1%
*Incl one additional pay period. 14
 
< 0.1%
*Elected to forego salary & bonus for stock opt. grants. 13
 
< 0.1%
*Includes portion of bonus paid in options. 13
 
< 0.1%
Other values (1403) 1855
 
6.4%
(Missing) 26778
92.5%

Length

2023-04-27T15:13:44.988657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in 887
 
5.4%
bonus 788
 
4.8%
incl 710
 
4.3%
of 648
 
3.9%
paid 514
 
3.1%
stock 436
 
2.6%
excl 434
 
2.6%
includes 347
 
2.1%
restricted 277
 
1.7%
to 276
 
1.7%
Other values (2036) 11160
67.7%

Most occurring characters

ValueCountFrequency (%)
14337
 
13.2%
n 7681
 
7.0%
e 7166
 
6.6%
o 6662
 
6.1%
i 5552
 
5.1%
t 5404
 
5.0%
s 5172
 
4.7%
r 4651
 
4.3%
a 4459
 
4.1%
c 4014
 
3.7%
Other values (67) 43912
40.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 72303
66.3%
Space Separator 14337
 
13.2%
Decimal Number 8864
 
8.1%
Other Punctuation 8070
 
7.4%
Uppercase Letter 3825
 
3.5%
Currency Symbol 1248
 
1.1%
Dash Punctuation 245
 
0.2%
Close Punctuation 50
 
< 0.1%
Open Punctuation 50
 
< 0.1%
Final Punctuation 13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 7681
10.6%
e 7166
 
9.9%
o 6662
 
9.2%
i 5552
 
7.7%
t 5404
 
7.5%
s 5172
 
7.2%
r 4651
 
6.4%
a 4459
 
6.2%
c 4014
 
5.6%
l 3478
 
4.8%
Other values (16) 18064
25.0%
Uppercase Letter
ValueCountFrequency (%)
I 1169
30.6%
E 617
16.1%
C 330
 
8.6%
S 246
 
6.4%
M 216
 
5.6%
P 166
 
4.3%
T 155
 
4.1%
A 134
 
3.5%
R 123
 
3.2%
O 106
 
2.8%
Other values (15) 563
14.7%
Decimal Number
ValueCountFrequency (%)
0 2592
29.2%
1 1031
 
11.6%
5 848
 
9.6%
2 827
 
9.3%
9 731
 
8.2%
3 688
 
7.8%
4 595
 
6.7%
8 523
 
5.9%
7 518
 
5.8%
6 511
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 3845
47.6%
* 2245
27.8%
, 1422
 
17.6%
/ 255
 
3.2%
& 147
 
1.8%
' 76
 
0.9%
% 68
 
0.8%
; 12
 
0.1%
Math Symbol
ValueCountFrequency (%)
= 4
80.0%
+ 1
 
20.0%
Space Separator
ValueCountFrequency (%)
14337
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1248
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 245
100.0%
Close Punctuation
ValueCountFrequency (%)
) 50
100.0%
Open Punctuation
ValueCountFrequency (%)
( 50
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 76128
69.8%
Common 32882
30.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 7681
 
10.1%
e 7166
 
9.4%
o 6662
 
8.8%
i 5552
 
7.3%
t 5404
 
7.1%
s 5172
 
6.8%
r 4651
 
6.1%
a 4459
 
5.9%
c 4014
 
5.3%
l 3478
 
4.6%
Other values (41) 21889
28.8%
Common
ValueCountFrequency (%)
14337
43.6%
. 3845
 
11.7%
0 2592
 
7.9%
* 2245
 
6.8%
, 1422
 
4.3%
$ 1248
 
3.8%
1 1031
 
3.1%
5 848
 
2.6%
2 827
 
2.5%
9 731
 
2.2%
Other values (16) 3756
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108997
> 99.9%
Punctuation 13
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14337
 
13.2%
n 7681
 
7.0%
e 7166
 
6.6%
o 6662
 
6.1%
i 5552
 
5.1%
t 5404
 
5.0%
s 5172
 
4.7%
r 4651
 
4.3%
a 4459
 
4.1%
c 4014
 
3.7%
Other values (66) 43899
40.3%
Punctuation
ValueCountFrequency (%)
’ 13
100.0%

GVKEY
Real number (ℝ)

Distinct386
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17473.837
Minimum1045
Maximum260774
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size226.3 KiB
2023-04-27T15:13:45.154066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1045
5-th percentile1678
Q14510
median8099
Q314418
95-th percentile65609
Maximum260774
Range259729
Interquartile range (IQR)9908

Descriptive statistics

Standard deviation28499.711
Coefficient of variation (CV)1.6309934
Kurtosis13.215268
Mean17473.837
Median Absolute Deviation (MAD)4042
Skewness3.4688056
Sum5.0591999 × 108
Variance8.1223355 × 108
MonotonicityIncreasing
2023-04-27T15:13:45.538588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8264 138
 
0.5%
9846 124
 
0.4%
13988 118
 
0.4%
8549 112
 
0.4%
4839 111
 
0.4%
3439 109
 
0.4%
7343 108
 
0.4%
1487 107
 
0.4%
8539 106
 
0.4%
11506 106
 
0.4%
Other values (376) 27814
96.1%
ValueCountFrequency (%)
1045 91
0.3%
1075 89
0.3%
1078 90
0.3%
1161 95
0.3%
1209 97
0.3%
1230 89
0.3%
1300 89
0.3%
1327 58
0.2%
1380 84
0.3%
1440 89
0.3%
ValueCountFrequency (%)
260774 20
0.1%
165993 15
0.1%
164708 15
0.1%
160329 14
< 0.1%
155394 22
0.1%
150937 26
0.1%
149337 15
0.1%
149070 22
0.1%
145977 25
0.1%
145701 33
0.1%

EXECID
Real number (ℝ)

Distinct5084
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12805.586
Minimum3
Maximum34976
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size226.3 KiB
2023-04-27T15:13:45.687145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile717
Q13661
median12604
Q320823
95-th percentile27633
Maximum34976
Range34973
Interquartile range (IQR)17162

Descriptive statistics

Standard deviation9132.5122
Coefficient of variation (CV)0.71316629
Kurtosis-1.2405848
Mean12805.586
Median Absolute Deviation (MAD)8600
Skewness0.20307487
Sum3.7076013 × 108
Variance83402779
MonotonicityNot monotonic
2023-04-27T15:13:45.833332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3765 16
 
0.1%
5669 15
 
0.1%
116 15
 
0.1%
3434 14
 
< 0.1%
4228 14
 
< 0.1%
1030 14
 
< 0.1%
5820 14
 
< 0.1%
125 14
 
< 0.1%
9752 14
 
< 0.1%
9753 14
 
< 0.1%
Other values (5074) 28809
99.5%
ValueCountFrequency (%)
3 7
< 0.1%
7 10
< 0.1%
8 12
< 0.1%
10 6
< 0.1%
13 14
< 0.1%
15 10
< 0.1%
26 11
< 0.1%
28 3
 
< 0.1%
36 4
 
< 0.1%
39 2
 
< 0.1%
ValueCountFrequency (%)
34976 2
 
< 0.1%
34974 1
 
< 0.1%
34965 2
 
< 0.1%
34964 2
 
< 0.1%
34963 5
< 0.1%
34897 3
< 0.1%
34896 4
< 0.1%
34895 4
< 0.1%
34406 5
< 0.1%
34404 2
 
< 0.1%

YEAR
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1999.008
Minimum1992
Maximum2005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size226.3 KiB
2023-04-27T15:13:45.970114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1992
5-th percentile1993
Q11996
median1999
Q32002
95-th percentile2005
Maximum2005
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.8733942
Coefficient of variation (CV)0.0019376582
Kurtosis-1.1161784
Mean1999.008
Median Absolute Deviation (MAD)3
Skewness-0.16344235
Sum57877279
Variance15.003183
MonotonicityNot monotonic
2023-04-27T15:13:46.088590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2003 2458
 
8.5%
2002 2410
 
8.3%
2001 2344
 
8.1%
2004 2315
 
8.0%
2000 2304
 
8.0%
1999 2275
 
7.9%
1998 2188
 
7.6%
2005 2075
 
7.2%
1997 2027
 
7.0%
1996 1900
 
6.6%
Other values (4) 6657
23.0%
ValueCountFrequency (%)
1992 1399
4.8%
1993 1653
5.7%
1994 1774
6.1%
1995 1831
6.3%
1996 1900
6.6%
1997 2027
7.0%
1998 2188
7.6%
1999 2275
7.9%
2000 2304
8.0%
2001 2344
8.1%
ValueCountFrequency (%)
2005 2075
7.2%
2004 2315
8.0%
2003 2458
8.5%
2002 2410
8.3%
2001 2344
8.1%
2000 2304
8.0%
1999 2275
7.9%
1998 2188
7.6%
1997 2027
7.0%
1996 1900
6.6%

BECAMECEO
Categorical

HIGH CARDINALITY  MISSING 

Distinct559
Distinct (%)7.0%
Missing20979
Missing (%)72.5%
Memory size226.3 KiB
2000-01-01
 
197
2001-01-01
 
143
1998-01-01
 
140
1994-01-01
 
113
1990-01-01
 
109
Other values (554)
7272 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters79740
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.2%

Sample

1st row1985-03-01
2nd row1998-05-20
3rd row1985-03-01
4th row1998-05-20
5th row1985-03-01

Common Values

ValueCountFrequency (%)
2000-01-01 197
 
0.7%
2001-01-01 143
 
0.5%
1998-01-01 140
 
0.5%
1994-01-01 113
 
0.4%
1990-01-01 109
 
0.4%
1993-01-01 101
 
0.3%
1999-01-01 98
 
0.3%
1997-01-01 91
 
0.3%
2001-02-01 82
 
0.3%
2002-01-01 80
 
0.3%
Other values (549) 6820
 
23.6%
(Missing) 20979
72.5%

Length

2023-04-27T15:13:46.206109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2000-01-01 197
 
2.5%
2001-01-01 143
 
1.8%
1998-01-01 140
 
1.8%
1994-01-01 113
 
1.4%
1990-01-01 109
 
1.4%
1993-01-01 101
 
1.3%
1999-01-01 98
 
1.2%
1997-01-01 91
 
1.1%
2001-02-01 82
 
1.0%
2002-01-01 80
 
1.0%
Other values (549) 6820
85.5%

Most occurring characters

ValueCountFrequency (%)
0 21186
26.6%
1 17139
21.5%
- 15948
20.0%
9 9019
11.3%
2 5809
 
7.3%
8 2266
 
2.8%
7 2048
 
2.6%
5 1826
 
2.3%
4 1556
 
2.0%
3 1535
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63792
80.0%
Dash Punctuation 15948
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21186
33.2%
1 17139
26.9%
9 9019
14.1%
2 5809
 
9.1%
8 2266
 
3.6%
7 2048
 
3.2%
5 1826
 
2.9%
4 1556
 
2.4%
3 1535
 
2.4%
6 1408
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 15948
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 79740
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21186
26.6%
1 17139
21.5%
- 15948
20.0%
9 9019
11.3%
2 5809
 
7.3%
8 2266
 
2.8%
7 2048
 
2.6%
5 1826
 
2.3%
4 1556
 
2.0%
3 1535
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21186
26.6%
1 17139
21.5%
- 15948
20.0%
9 9019
11.3%
2 5809
 
7.3%
8 2266
 
2.8%
7 2048
 
2.6%
5 1826
 
2.3%
4 1556
 
2.0%
3 1535
 
1.9%

JOINED_CO
Categorical

HIGH CARDINALITY  MISSING 

Distinct564
Distinct (%)5.9%
Missing19336
Missing (%)66.8%
Memory size226.3 KiB
1979-01-01
 
195
1976-01-01
 
162
1984-01-01
 
147
1975-01-01
 
139
1963-01-01
 
139
Other values (559)
8835 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters96170
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)0.5%

Sample

1st row1973-04-01
2nd row1973-04-01
3rd row1973-04-01
4th row1973-04-01
5th row1973-04-01

Common Values

ValueCountFrequency (%)
1979-01-01 195
 
0.7%
1976-01-01 162
 
0.6%
1984-01-01 147
 
0.5%
1975-01-01 139
 
0.5%
1963-01-01 139
 
0.5%
1982-01-01 139
 
0.5%
1969-01-01 137
 
0.5%
1966-01-01 135
 
0.5%
1983-01-01 132
 
0.5%
1971-01-01 131
 
0.5%
Other values (554) 8161
28.2%
(Missing) 19336
66.8%

Length

2023-04-27T15:13:46.318993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1979-01-01 195
 
2.0%
1976-01-01 162
 
1.7%
1984-01-01 147
 
1.5%
1975-01-01 139
 
1.4%
1963-01-01 139
 
1.4%
1982-01-01 139
 
1.4%
1969-01-01 137
 
1.4%
1966-01-01 135
 
1.4%
1983-01-01 132
 
1.4%
1971-01-01 131
 
1.4%
Other values (554) 8161
84.9%

Most occurring characters

ValueCountFrequency (%)
1 24555
25.5%
0 21635
22.5%
- 19234
20.0%
9 13214
13.7%
2 3614
 
3.8%
8 3119
 
3.2%
6 2830
 
2.9%
7 2789
 
2.9%
5 1822
 
1.9%
4 1689
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76936
80.0%
Dash Punctuation 19234
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 24555
31.9%
0 21635
28.1%
9 13214
17.2%
2 3614
 
4.7%
8 3119
 
4.1%
6 2830
 
3.7%
7 2789
 
3.6%
5 1822
 
2.4%
4 1689
 
2.2%
3 1669
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 19234
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 96170
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 24555
25.5%
0 21635
22.5%
- 19234
20.0%
9 13214
13.7%
2 3614
 
3.8%
8 3119
 
3.2%
6 2830
 
2.9%
7 2789
 
2.9%
5 1822
 
1.9%
4 1689
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 24555
25.5%
0 21635
22.5%
- 19234
20.0%
9 13214
13.7%
2 3614
 
3.8%
8 3119
 
3.2%
6 2830
 
2.9%
7 2789
 
2.9%
5 1822
 
1.9%
4 1689
 
1.8%

REJOIN
Categorical

Distinct22
Distinct (%)13.3%
Missing28787
Missing (%)99.4%
Memory size226.3 KiB
1991-01-01
15 
1994-01-01
14 
1995-11-01
11 
1976-01-01
11 
1995-02-01
11 
Other values (17)
104 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1660
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row2001-07-03
2nd row2001-07-03
3rd row2001-07-03
4th row2001-07-03
5th row2001-07-03

Common Values

ValueCountFrequency (%)
1991-01-01 15
 
0.1%
1994-01-01 14
 
< 0.1%
1995-11-01 11
 
< 0.1%
1976-01-01 11
 
< 0.1%
1995-02-01 11
 
< 0.1%
2001-07-03 11
 
< 0.1%
1983-01-01 11
 
< 0.1%
2003-12-01 9
 
< 0.1%
2002-04-01 9
 
< 0.1%
2001-09-01 9
 
< 0.1%
Other values (12) 55
 
0.2%
(Missing) 28787
99.4%

Length

2023-04-27T15:13:46.462880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1991-01-01 15
 
9.0%
1994-01-01 14
 
8.4%
1995-11-01 11
 
6.6%
1976-01-01 11
 
6.6%
1995-02-01 11
 
6.6%
2001-07-03 11
 
6.6%
1983-01-01 11
 
6.6%
2003-12-01 9
 
5.4%
2002-04-01 9
 
5.4%
2001-09-01 9
 
5.4%
Other values (12) 55
33.1%

Most occurring characters

ValueCountFrequency (%)
0 437
26.3%
1 397
23.9%
- 332
20.0%
9 177
10.7%
2 136
 
8.2%
3 47
 
2.8%
5 39
 
2.3%
8 31
 
1.9%
4 23
 
1.4%
7 23
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1328
80.0%
Dash Punctuation 332
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 437
32.9%
1 397
29.9%
9 177
13.3%
2 136
 
10.2%
3 47
 
3.5%
5 39
 
2.9%
8 31
 
2.3%
4 23
 
1.7%
7 23
 
1.7%
6 18
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 332
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1660
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 437
26.3%
1 397
23.9%
- 332
20.0%
9 177
10.7%
2 136
 
8.2%
3 47
 
2.8%
5 39
 
2.3%
8 31
 
1.9%
4 23
 
1.4%
7 23
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 437
26.3%
1 397
23.9%
- 332
20.0%
9 177
10.7%
2 136
 
8.2%
3 47
 
2.8%
5 39
 
2.3%
8 31
 
1.9%
4 23
 
1.4%
7 23
 
1.4%

LEFTOFC
Categorical

HIGH CARDINALITY  MISSING 

Distinct659
Distinct (%)9.0%
Missing21623
Missing (%)74.7%
Memory size226.3 KiB
2005-12-31
 
98
2005-07-01
 
66
2007-01-01
 
63
2007-07-01
 
59
2008-01-01
 
58
Other values (654)
6986 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters73300
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st row1998-05-20
2nd row2003-04-24
3rd row1998-05-20
4th row2003-04-24
5th row1998-05-20

Common Values

ValueCountFrequency (%)
2005-12-31 98
 
0.3%
2005-07-01 66
 
0.2%
2007-01-01 63
 
0.2%
2007-07-01 59
 
0.2%
2008-01-01 58
 
0.2%
2013-01-01 51
 
0.2%
2004-07-01 48
 
0.2%
1998-12-31 47
 
0.2%
2000-12-31 46
 
0.2%
2011-01-01 45
 
0.2%
Other values (649) 6749
 
23.3%
(Missing) 21623
74.7%

Length

2023-04-27T15:13:46.586037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2005-12-31 98
 
1.3%
2005-07-01 66
 
0.9%
2007-01-01 63
 
0.9%
2007-07-01 59
 
0.8%
2008-01-01 58
 
0.8%
2013-01-01 51
 
0.7%
2004-07-01 48
 
0.7%
1998-12-31 47
 
0.6%
2000-12-31 46
 
0.6%
2011-01-01 45
 
0.6%
Other values (649) 6749
92.1%

Most occurring characters

ValueCountFrequency (%)
0 20742
28.3%
- 14660
20.0%
1 12873
17.6%
2 9966
13.6%
9 3803
 
5.2%
3 3125
 
4.3%
5 1933
 
2.6%
4 1785
 
2.4%
7 1732
 
2.4%
6 1368
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58640
80.0%
Dash Punctuation 14660
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20742
35.4%
1 12873
22.0%
2 9966
17.0%
9 3803
 
6.5%
3 3125
 
5.3%
5 1933
 
3.3%
4 1785
 
3.0%
7 1732
 
3.0%
6 1368
 
2.3%
8 1313
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 14660
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 73300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20742
28.3%
- 14660
20.0%
1 12873
17.6%
2 9966
13.6%
9 3803
 
5.2%
3 3125
 
4.3%
5 1933
 
2.6%
4 1785
 
2.4%
7 1732
 
2.4%
6 1368
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20742
28.3%
- 14660
20.0%
1 12873
17.6%
2 9966
13.6%
9 3803
 
5.2%
3 3125
 
4.3%
5 1933
 
2.6%
4 1785
 
2.4%
7 1732
 
2.4%
6 1368
 
1.9%

LEFTCO
Categorical

HIGH CARDINALITY  MISSING 

Distinct780
Distinct (%)8.8%
Missing20082
Missing (%)69.4%
Memory size226.3 KiB
2005-12-31
 
165
2007-12-31
 
147
2004-12-31
 
130
2008-12-31
 
126
1999-12-31
 
112
Other values (775)
8191 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters88710
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.2%

Sample

1st row1998-12-31
2nd row2002-04-01
3rd row2003-04-24
4th row2002-09-17
5th row1995-01-15

Common Values

ValueCountFrequency (%)
2005-12-31 165
 
0.6%
2007-12-31 147
 
0.5%
2004-12-31 130
 
0.4%
2008-12-31 126
 
0.4%
1999-12-31 112
 
0.4%
2002-12-31 97
 
0.3%
2003-12-31 93
 
0.3%
1998-12-31 83
 
0.3%
2006-12-31 83
 
0.3%
2002-01-01 61
 
0.2%
Other values (770) 7774
 
26.9%
(Missing) 20082
69.4%

Length

2023-04-27T15:13:46.706396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2005-12-31 165
 
1.9%
2007-12-31 147
 
1.7%
2004-12-31 130
 
1.5%
2008-12-31 126
 
1.4%
1999-12-31 112
 
1.3%
2002-12-31 97
 
1.1%
2003-12-31 93
 
1.0%
1998-12-31 83
 
0.9%
2006-12-31 83
 
0.9%
2002-01-01 61
 
0.7%
Other values (770) 7774
87.6%

Most occurring characters

ValueCountFrequency (%)
0 25332
28.6%
- 17742
20.0%
1 13635
15.4%
2 11084
12.5%
9 5938
 
6.7%
3 4870
 
5.5%
7 2226
 
2.5%
5 2057
 
2.3%
8 2028
 
2.3%
6 1924
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70968
80.0%
Dash Punctuation 17742
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25332
35.7%
1 13635
19.2%
2 11084
15.6%
9 5938
 
8.4%
3 4870
 
6.9%
7 2226
 
3.1%
5 2057
 
2.9%
8 2028
 
2.9%
6 1924
 
2.7%
4 1874
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 17742
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 88710
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25332
28.6%
- 17742
20.0%
1 13635
15.4%
2 11084
12.5%
9 5938
 
6.7%
3 4870
 
5.5%
7 2226
 
2.5%
5 2057
 
2.3%
8 2028
 
2.3%
6 1924
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25332
28.6%
- 17742
20.0%
1 13635
15.4%
2 11084
12.5%
9 5938
 
6.7%
3 4870
 
5.5%
7 2226
 
2.5%
5 2057
 
2.3%
8 2028
 
2.3%
6 1924
 
2.2%

RELEFT
Categorical

Distinct9
Distinct (%)12.9%
Missing28883
Missing (%)99.8%
Memory size226.3 KiB
2006-10-18
12 
2002-07-01
11 
2006-12-31
11 
2002-04-25
11 
2005-04-01
Other values (4)
16 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters700
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2002-07-01
2nd row2002-07-01
3rd row2002-07-01
4th row2002-07-01
5th row2002-07-01

Common Values

ValueCountFrequency (%)
2006-10-18 12
 
< 0.1%
2002-07-01 11
 
< 0.1%
2006-12-31 11
 
< 0.1%
2002-04-25 11
 
< 0.1%
2005-04-01 9
 
< 0.1%
2005-02-01 6
 
< 0.1%
1997-07-01 6
 
< 0.1%
1994-12-31 2
 
< 0.1%
1996-11-20 2
 
< 0.1%
(Missing) 28883
99.8%

Length

2023-04-27T15:13:46.827015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T15:13:46.976825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2006-10-18 12
17.1%
2002-07-01 11
15.7%
2006-12-31 11
15.7%
2002-04-25 11
15.7%
2005-04-01 9
12.9%
2005-02-01 6
8.6%
1997-07-01 6
8.6%
1994-12-31 2
 
2.9%
1996-11-20 2
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 209
29.9%
- 140
20.0%
2 114
16.3%
1 96
13.7%
5 26
 
3.7%
6 25
 
3.6%
7 23
 
3.3%
4 22
 
3.1%
9 20
 
2.9%
3 13
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 560
80.0%
Dash Punctuation 140
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 209
37.3%
2 114
20.4%
1 96
17.1%
5 26
 
4.6%
6 25
 
4.5%
7 23
 
4.1%
4 22
 
3.9%
9 20
 
3.6%
3 13
 
2.3%
8 12
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 209
29.9%
- 140
20.0%
2 114
16.3%
1 96
13.7%
5 26
 
3.7%
6 25
 
3.6%
7 23
 
3.3%
4 22
 
3.1%
9 20
 
2.9%
3 13
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 209
29.9%
- 140
20.0%
2 114
16.3%
1 96
13.7%
5 26
 
3.7%
6 25
 
3.6%
7 23
 
3.3%
4 22
 
3.1%
9 20
 
2.9%
3 13
 
1.9%

PCEO
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing28285
Missing (%)97.7%
Memory size226.3 KiB
CEO
668 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2004
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCEO
2nd rowCEO
3rd rowCEO
4th rowCEO
5th rowCEO

Common Values

ValueCountFrequency (%)
CEO 668
 
2.3%
(Missing) 28285
97.7%

Length

2023-04-27T15:13:47.135982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T15:13:47.250129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ceo 668
100.0%

Most occurring characters

ValueCountFrequency (%)
C 668
33.3%
E 668
33.3%
O 668
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2004
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 668
33.3%
E 668
33.3%
O 668
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 2004
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 668
33.3%
E 668
33.3%
O 668
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 668
33.3%
E 668
33.3%
O 668
33.3%

PCFO
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.1%
Missing28858
Missing (%)99.7%
Memory size226.3 KiB
CFO
95 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters285
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCFO
2nd rowCFO
3rd rowCFO
4th rowCFO
5th rowCFO

Common Values

ValueCountFrequency (%)
CFO 95
 
0.3%
(Missing) 28858
99.7%

Length

2023-04-27T15:13:47.359284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T15:13:47.466574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
cfo 95
100.0%

Most occurring characters

ValueCountFrequency (%)
C 95
33.3%
F 95
33.3%
O 95
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 285
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 95
33.3%
F 95
33.3%
O 95
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 285
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 95
33.3%
F 95
33.3%
O 95
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 95
33.3%
F 95
33.3%
O 95
33.3%

TITLE
Categorical

Distinct2655
Distinct (%)9.2%
Missing5
Missing (%)< 0.1%
Memory size226.3 KiB
chairman & CEO
 
1093
chairman
 
965
executive vp
 
891
vice chairman
 
681
Executive Chairman
 
585
Other values (2650)
24733 

Length

Max length231
Median length175
Mean length29.985664
Min length3

Characters and Unicode

Total characters868025
Distinct characters64
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125 ?
Unique (%)0.4%

Sample

1st rowchmn., pres. & CEO
2nd rowvice chairman
3rd rowchairman & CEO
4th rowexecutive vp; executive vp-marketing & planning-American
5th rowsr. v-p

Common Values

ValueCountFrequency (%)
chairman & CEO 1093
 
3.8%
chairman 965
 
3.3%
executive vp 891
 
3.1%
vice chairman 681
 
2.4%
Executive Chairman 585
 
2.0%
exec. v-p 569
 
2.0%
senior vp 497
 
1.7%
sr. v-p 378
 
1.3%
chmn. & CEO 377
 
1.3%
president & chief operating officer 349
 
1.2%
Other values (2645) 22563
77.9%

Length

2023-04-27T15:13:47.598366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11818
 
9.5%
executive 8796
 
7.1%
chairman 8224
 
6.6%
president 5684
 
4.6%
chief 5639
 
4.5%
officer 5562
 
4.5%
vp 5002
 
4.0%
of 4367
 
3.5%
and 4237
 
3.4%
vice 3989
 
3.2%
Other values (2066) 61004
49.1%

Most occurring characters

ValueCountFrequency (%)
e 99729
 
11.5%
95491
 
11.0%
i 69579
 
8.0%
r 58312
 
6.7%
n 52870
 
6.1%
c 45866
 
5.3%
a 45528
 
5.2%
t 37407
 
4.3%
o 35865
 
4.1%
s 29418
 
3.4%
Other values (54) 297960
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 668144
77.0%
Space Separator 95491
 
11.0%
Uppercase Letter 64868
 
7.5%
Other Punctuation 26342
 
3.0%
Dash Punctuation 13081
 
1.5%
Close Punctuation 40
 
< 0.1%
Open Punctuation 40
 
< 0.1%
Decimal Number 19
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 99729
14.9%
i 69579
10.4%
r 58312
 
8.7%
n 52870
 
7.9%
c 45866
 
6.9%
a 45528
 
6.8%
t 37407
 
5.6%
o 35865
 
5.4%
s 29418
 
4.4%
f 27894
 
4.2%
Other values (16) 165676
24.8%
Uppercase Letter
ValueCountFrequency (%)
C 16901
26.1%
E 10838
16.7%
O 9424
14.5%
P 5460
 
8.4%
V 2976
 
4.6%
S 2825
 
4.4%
F 2208
 
3.4%
A 2122
 
3.3%
D 1872
 
2.9%
M 1860
 
2.9%
Other values (16) 8382
12.9%
Other Punctuation
ValueCountFrequency (%)
& 11989
45.5%
. 7912
30.0%
, 5008
19.0%
; 1291
 
4.9%
/ 79
 
0.3%
' 63
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 13
68.4%
4 6
31.6%
Space Separator
ValueCountFrequency (%)
95491
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13081
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 733012
84.4%
Common 135013
 
15.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 99729
13.6%
i 69579
 
9.5%
r 58312
 
8.0%
n 52870
 
7.2%
c 45866
 
6.3%
a 45528
 
6.2%
t 37407
 
5.1%
o 35865
 
4.9%
s 29418
 
4.0%
f 27894
 
3.8%
Other values (42) 230544
31.5%
Common
ValueCountFrequency (%)
95491
70.7%
- 13081
 
9.7%
& 11989
 
8.9%
. 7912
 
5.9%
, 5008
 
3.7%
; 1291
 
1.0%
/ 79
 
0.1%
' 63
 
< 0.1%
) 40
 
< 0.1%
( 40
 
< 0.1%
Other values (2) 19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 868025
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 99729
 
11.5%
95491
 
11.0%
i 69579
 
8.0%
r 58312
 
6.7%
n 52870
 
6.1%
c 45866
 
5.3%
a 45528
 
5.2%
t 37407
 
4.3%
o 35865
 
4.1%
s 29418
 
3.4%
Other values (54) 297960
34.3%

REASON
Categorical

Distinct4
Distinct (%)< 0.1%
Missing20141
Missing (%)69.6%
Memory size226.3 KiB
RETIRED
5619 
RESIGNED
2204 
UNKNOWN
856 
DECEASED
 
133

Length

Max length8
Median length7
Mean length7.2652065
Min length7

Characters and Unicode

Total characters64021
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRETIRED
2nd rowRETIRED
3rd rowRESIGNED
4th rowRETIRED
5th rowRETIRED

Common Values

ValueCountFrequency (%)
RETIRED 5619
 
19.4%
RESIGNED 2204
 
7.6%
UNKNOWN 856
 
3.0%
DECEASED 133
 
0.5%
(Missing) 20141
69.6%

Length

2023-04-27T15:13:47.746546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T15:13:47.882489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
retired 5619
63.8%
resigned 2204
 
25.0%
unknown 856
 
9.7%
deceased 133
 
1.5%

Most occurring characters

ValueCountFrequency (%)
E 16045
25.1%
R 13442
21.0%
D 8089
12.6%
I 7823
12.2%
T 5619
 
8.8%
N 4772
 
7.5%
S 2337
 
3.7%
G 2204
 
3.4%
U 856
 
1.3%
K 856
 
1.3%
Other values (4) 1978
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 64021
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 16045
25.1%
R 13442
21.0%
D 8089
12.6%
I 7823
12.2%
T 5619
 
8.8%
N 4772
 
7.5%
S 2337
 
3.7%
G 2204
 
3.4%
U 856
 
1.3%
K 856
 
1.3%
Other values (4) 1978
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 64021
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 16045
25.1%
R 13442
21.0%
D 8089
12.6%
I 7823
12.2%
T 5619
 
8.8%
N 4772
 
7.5%
S 2337
 
3.7%
G 2204
 
3.4%
U 856
 
1.3%
K 856
 
1.3%
Other values (4) 1978
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 16045
25.1%
R 13442
21.0%
D 8089
12.6%
I 7823
12.2%
T 5619
 
8.8%
N 4772
 
7.5%
S 2337
 
3.7%
G 2204
 
3.4%
U 856
 
1.3%
K 856
 
1.3%
Other values (4) 1978
 
3.1%

EXEC_LNAME
Categorical

Distinct3976
Distinct (%)13.7%
Missing6
Missing (%)< 0.1%
Memory size226.3 KiB
Smith
 
208
Johnson
 
202
Miller
 
124
Lewis
 
88
Campbell
 
87
Other values (3971)
28238 

Length

Max length20
Median length18
Mean length7.244274
Min length2

Characters and Unicode

Total characters209700
Distinct characters59
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique152 ?
Unique (%)0.5%

Sample

1st rowCrandall
2nd rowBaker
3rd rowCarty
4th rowGunn
5th rowHopper

Common Values

ValueCountFrequency (%)
Smith 208
 
0.7%
Johnson 202
 
0.7%
Miller 124
 
0.4%
Lewis 88
 
0.3%
Campbell 87
 
0.3%
Williams 86
 
0.3%
Thompson 75
 
0.3%
Kelly 71
 
0.2%
Ryan 70
 
0.2%
Sullivan 67
 
0.2%
Other values (3966) 27869
96.3%

Length

2023-04-27T15:13:48.063546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jr 1640
 
5.0%
iii 412
 
1.3%
ph.d 256
 
0.8%
smith 247
 
0.8%
johnson 221
 
0.7%
cpa 207
 
0.6%
miller 137
 
0.4%
ii 114
 
0.3%
m.b.a 105
 
0.3%
lewis 97
 
0.3%
Other values (3839) 29357
89.5%

Most occurring characters

ValueCountFrequency (%)
e 20427
 
9.7%
r 16600
 
7.9%
a 15154
 
7.2%
n 15094
 
7.2%
o 12613
 
6.0%
l 12063
 
5.8%
i 11171
 
5.3%
s 8595
 
4.1%
t 7788
 
3.7%
h 5903
 
2.8%
Other values (49) 84292
40.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 161847
77.2%
Uppercase Letter 36643
 
17.5%
Other Punctuation 7274
 
3.5%
Space Separator 3846
 
1.8%
Dash Punctuation 74
 
< 0.1%
Open Punctuation 11
 
< 0.1%
Close Punctuation 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 20427
12.6%
r 16600
10.3%
a 15154
9.4%
n 15094
9.3%
o 12613
 
7.8%
l 12063
 
7.5%
i 11171
 
6.9%
s 8595
 
5.3%
t 7788
 
4.8%
h 5903
 
3.6%
Other values (16) 36439
22.5%
Uppercase Letter
ValueCountFrequency (%)
S 3315
 
9.0%
B 3101
 
8.5%
M 3066
 
8.4%
C 2747
 
7.5%
J 2554
 
7.0%
H 1926
 
5.3%
D 1915
 
5.2%
W 1831
 
5.0%
P 1802
 
4.9%
G 1767
 
4.8%
Other values (16) 12619
34.4%
Other Punctuation
ValueCountFrequency (%)
. 3602
49.5%
, 3398
46.7%
' 274
 
3.8%
Space Separator
ValueCountFrequency (%)
3846
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 74
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 198490
94.7%
Common 11210
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20427
 
10.3%
r 16600
 
8.4%
a 15154
 
7.6%
n 15094
 
7.6%
o 12613
 
6.4%
l 12063
 
6.1%
i 11171
 
5.6%
s 8595
 
4.3%
t 7788
 
3.9%
h 5903
 
3.0%
Other values (42) 73082
36.8%
Common
ValueCountFrequency (%)
3846
34.3%
. 3602
32.1%
, 3398
30.3%
' 274
 
2.4%
- 74
 
0.7%
( 11
 
0.1%
) 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 209700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 20427
 
9.7%
r 16600
 
7.9%
a 15154
 
7.2%
n 15094
 
7.2%
o 12613
 
6.0%
l 12063
 
5.8%
i 11171
 
5.3%
s 8595
 
4.1%
t 7788
 
3.7%
h 5903
 
2.8%
Other values (49) 84292
40.2%

EXEC_FNAME
Categorical

Distinct811
Distinct (%)2.8%
Missing6
Missing (%)< 0.1%
Memory size226.3 KiB
John
 
1856
Robert
 
1439
James
 
1336
William
 
1120
David
 
1079
Other values (806)
22117 

Length

Max length16
Median length14
Mean length5.4888589
Min length2

Characters and Unicode

Total characters158886
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)0.1%

Sample

1st rowRobert
2nd rowRobert
3rd rowDonald
4th rowMichael
5th rowMax

Common Values

ValueCountFrequency (%)
John 1856
 
6.4%
Robert 1439
 
5.0%
James 1336
 
4.6%
William 1120
 
3.9%
David 1079
 
3.7%
Michael 956
 
3.3%
Richard 937
 
3.2%
Thomas 793
 
2.7%
Charles 500
 
1.7%
Joseph 403
 
1.4%
Other values (801) 18528
64.0%

Length

2023-04-27T15:13:48.269992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
john 1859
 
6.4%
robert 1444
 
5.0%
james 1336
 
4.6%
william 1134
 
3.9%
david 1084
 
3.7%
michael 964
 
3.3%
richard 937
 
3.2%
thomas 793
 
2.7%
charles 503
 
1.7%
joseph 403
 
1.4%
Other values (788) 18600
64.0%

Most occurring characters

ValueCountFrequency (%)
e 15956
 
10.0%
a 15893
 
10.0%
r 12689
 
8.0%
n 9941
 
6.3%
i 9610
 
6.0%
o 9197
 
5.8%
l 8921
 
5.6%
h 7991
 
5.0%
d 5426
 
3.4%
t 5248
 
3.3%
Other values (45) 58014
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 127596
80.3%
Uppercase Letter 29151
 
18.3%
Other Punctuation 1948
 
1.2%
Space Separator 110
 
0.1%
Dash Punctuation 81
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 15956
12.5%
a 15893
12.5%
r 12689
9.9%
n 9941
 
7.8%
i 9610
 
7.5%
o 9197
 
7.2%
l 8921
 
7.0%
h 7991
 
6.3%
d 5426
 
4.3%
t 5248
 
4.1%
Other values (16) 26724
20.9%
Uppercase Letter
ValueCountFrequency (%)
J 5048
17.3%
R 3651
12.5%
D 2760
 
9.5%
M 2151
 
7.4%
W 1796
 
6.2%
G 1460
 
5.0%
S 1406
 
4.8%
C 1321
 
4.5%
T 1273
 
4.4%
P 1253
 
4.3%
Other values (16) 7032
24.1%
Other Punctuation
ValueCountFrequency (%)
. 1948
100.0%
Space Separator
ValueCountFrequency (%)
110
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 156747
98.7%
Common 2139
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 15956
 
10.2%
a 15893
 
10.1%
r 12689
 
8.1%
n 9941
 
6.3%
i 9610
 
6.1%
o 9197
 
5.9%
l 8921
 
5.7%
h 7991
 
5.1%
d 5426
 
3.5%
t 5248
 
3.3%
Other values (42) 55875
35.6%
Common
ValueCountFrequency (%)
. 1948
91.1%
110
 
5.1%
- 81
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 15956
 
10.0%
a 15893
 
10.0%
r 12689
 
8.0%
n 9941
 
6.3%
i 9610
 
6.0%
o 9197
 
5.8%
l 8921
 
5.6%
h 7991
 
5.0%
d 5426
 
3.4%
t 5248
 
3.3%
Other values (45) 58014
36.5%

GENDER
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size226.3 KiB
MALE
27817 
FEMALE
 
1136

Length

Max length6
Median length4
Mean length4.078472
Min length4

Characters and Unicode

Total characters118084
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowMALE
3rd rowMALE
4th rowMALE
5th rowMALE

Common Values

ValueCountFrequency (%)
MALE 27817
96.1%
FEMALE 1136
 
3.9%

Length

2023-04-27T15:13:48.434662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T15:13:48.553986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
male 27817
96.1%
female 1136
 
3.9%

Most occurring characters

ValueCountFrequency (%)
E 30089
25.5%
M 28953
24.5%
A 28953
24.5%
L 28953
24.5%
F 1136
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 118084
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 30089
25.5%
M 28953
24.5%
A 28953
24.5%
L 28953
24.5%
F 1136
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 118084
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 30089
25.5%
M 28953
24.5%
A 28953
24.5%
L 28953
24.5%
F 1136
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 30089
25.5%
M 28953
24.5%
A 28953
24.5%
L 28953
24.5%
F 1136
 
1.0%

PAGE
Real number (ℝ)

Distinct60
Distinct (%)0.3%
Missing7491
Missing (%)25.9%
Infinite0
Infinite (%)0.0%
Mean74.339763
Minimum46
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size226.3 KiB
2023-04-27T15:13:48.667804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile61
Q168
median74
Q380
95-th percentile89
Maximum111
Range65
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.759284
Coefficient of variation (CV)0.11782771
Kurtosis0.11442876
Mean74.339763
Median Absolute Deviation (MAD)6
Skewness0.27743578
Sum1595480
Variance76.725057
MonotonicityNot monotonic
2023-04-27T15:13:48.835343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 1195
 
4.1%
74 1045
 
3.6%
73 999
 
3.5%
71 949
 
3.3%
72 934
 
3.2%
79 933
 
3.2%
76 902
 
3.1%
69 876
 
3.0%
80 780
 
2.7%
67 776
 
2.7%
Other values (50) 12073
41.7%
(Missing) 7491
25.9%
ValueCountFrequency (%)
46 10
 
< 0.1%
48 2
 
< 0.1%
49 9
 
< 0.1%
51 10
 
< 0.1%
52 19
 
0.1%
53 31
 
0.1%
54 48
0.2%
55 43
0.1%
56 48
0.2%
57 94
0.3%
ValueCountFrequency (%)
111 3
 
< 0.1%
108 4
 
< 0.1%
106 39
0.1%
105 6
 
< 0.1%
104 6
 
< 0.1%
102 14
 
< 0.1%
101 13
 
< 0.1%
100 42
0.1%
99 9
 
< 0.1%
98 45
0.2%

CUSIP
Categorical

Distinct386
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size226.3 KiB
69331C10
 
138
28102010
 
124
80851310
 
118
20825C10
 
112
34537086
 
111
Other values (381)
28350 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters231624
Distinct characters33
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02376R10
2nd row02376R10
3rd row02376R10
4th row02376R10
5th row02376R10

Common Values

ValueCountFrequency (%)
69331C10 138
 
0.5%
28102010 124
 
0.4%
80851310 118
 
0.4%
20825C10 112
 
0.4%
34537086 111
 
0.4%
12589610 109
 
0.4%
59511210 108
 
0.4%
02687478 107
 
0.4%
30161N10 106
 
0.4%
96945710 106
 
0.4%
Other values (376) 27814
96.1%

Length

2023-04-27T15:13:49.410887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
69331c10 138
 
0.5%
28102010 124
 
0.4%
80851310 118
 
0.4%
20825c10 112
 
0.4%
34537086 111
 
0.4%
12589610 109
 
0.4%
59511210 108
 
0.4%
02687478 107
 
0.4%
30161n10 106
 
0.4%
96945710 106
 
0.4%
Other values (376) 27814
96.1%

Most occurring characters

ValueCountFrequency (%)
0 48712
21.0%
1 43855
18.9%
4 18076
 
7.8%
3 17242
 
7.4%
2 16657
 
7.2%
6 16590
 
7.2%
5 16346
 
7.1%
7 15283
 
6.6%
8 15216
 
6.6%
9 13560
 
5.9%
Other values (23) 10087
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 221537
95.6%
Uppercase Letter 10087
 
4.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 1251
 
12.4%
L 864
 
8.6%
R 720
 
7.1%
E 711
 
7.0%
C 644
 
6.4%
H 625
 
6.2%
P 548
 
5.4%
T 501
 
5.0%
V 468
 
4.6%
F 430
 
4.3%
Other values (13) 3325
33.0%
Decimal Number
ValueCountFrequency (%)
0 48712
22.0%
1 43855
19.8%
4 18076
 
8.2%
3 17242
 
7.8%
2 16657
 
7.5%
6 16590
 
7.5%
5 16346
 
7.4%
7 15283
 
6.9%
8 15216
 
6.9%
9 13560
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common 221537
95.6%
Latin 10087
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 1251
 
12.4%
L 864
 
8.6%
R 720
 
7.1%
E 711
 
7.0%
C 644
 
6.4%
H 625
 
6.2%
P 548
 
5.4%
T 501
 
5.0%
V 468
 
4.6%
F 430
 
4.3%
Other values (13) 3325
33.0%
Common
ValueCountFrequency (%)
0 48712
22.0%
1 43855
19.8%
4 18076
 
8.2%
3 17242
 
7.8%
2 16657
 
7.5%
6 16590
 
7.5%
5 16346
 
7.4%
7 15283
 
6.9%
8 15216
 
6.9%
9 13560
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48712
21.0%
1 43855
18.9%
4 18076
 
7.8%
3 17242
 
7.4%
2 16657
 
7.2%
6 16590
 
7.2%
5 16346
 
7.1%
7 15283
 
6.6%
8 15216
 
6.6%
9 13560
 
5.9%
Other values (23) 10087
 
4.4%

TICKER
Categorical

Distinct386
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size226.3 KiB
PCG
 
138
EIX
 
124
SCHW
 
118
COP
 
112
F
 
111
Other values (381)
28350 

Length

Max length5
Median length3
Mean length3.0498739
Min length1

Characters and Unicode

Total characters88303
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAAL
2nd rowAAL
3rd rowAAL
4th rowAAL
5th rowAAL

Common Values

ValueCountFrequency (%)
PCG 138
 
0.5%
EIX 124
 
0.4%
SCHW 118
 
0.4%
COP 112
 
0.4%
F 111
 
0.4%
CMS 109
 
0.4%
MU 108
 
0.4%
AIG 107
 
0.4%
EXC 106
 
0.4%
WMB 106
 
0.4%
Other values (376) 27814
96.1%

Length

2023-04-27T15:13:49.531806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pcg 138
 
0.5%
eix 124
 
0.4%
schw 118
 
0.4%
cop 112
 
0.4%
f 111
 
0.4%
cms 109
 
0.4%
mu 108
 
0.4%
aig 107
 
0.4%
exc 106
 
0.4%
wmb 106
 
0.4%
Other values (376) 27814
96.1%

Most occurring characters

ValueCountFrequency (%)
A 6535
 
7.4%
C 6531
 
7.4%
M 5668
 
6.4%
T 5323
 
6.0%
S 5189
 
5.9%
E 4742
 
5.4%
P 4642
 
5.3%
L 4310
 
4.9%
R 4277
 
4.8%
N 4205
 
4.8%
Other values (17) 36881
41.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 88175
99.9%
Other Punctuation 128
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 6535
 
7.4%
C 6531
 
7.4%
M 5668
 
6.4%
T 5323
 
6.0%
S 5189
 
5.9%
E 4742
 
5.4%
P 4642
 
5.3%
L 4310
 
4.9%
R 4277
 
4.9%
N 4205
 
4.8%
Other values (16) 36753
41.7%
Other Punctuation
ValueCountFrequency (%)
. 128
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88175
99.9%
Common 128
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 6535
 
7.4%
C 6531
 
7.4%
M 5668
 
6.4%
T 5323
 
6.0%
S 5189
 
5.9%
E 4742
 
5.4%
P 4642
 
5.3%
L 4310
 
4.9%
R 4277
 
4.9%
N 4205
 
4.8%
Other values (16) 36753
41.7%
Common
ValueCountFrequency (%)
. 128
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 6535
 
7.4%
C 6531
 
7.4%
M 5668
 
6.4%
T 5323
 
6.0%
S 5189
 
5.9%
E 4742
 
5.4%
P 4642
 
5.3%
L 4310
 
4.9%
R 4277
 
4.8%
N 4205
 
4.8%
Other values (17) 36881
41.8%

NAICS
Real number (ℝ)

Distinct178
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean354630.62
Minimum42
Maximum999977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size226.3 KiB
2023-04-27T15:13:49.669124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile2211
Q1316210
median334516
Q3518210
95-th percentile561450
Maximum999977
Range999935
Interquartile range (IQR)202000

Descriptive statistics

Standard deviation188315.87
Coefficient of variation (CV)0.53101977
Kurtosis0.057663453
Mean354630.62
Median Absolute Deviation (MAD)147595
Skewness-0.40580918
Sum1.026762 × 1010
Variance3.5462867 × 1010
MonotonicityNot monotonic
2023-04-27T15:13:49.824620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
522110 1465
 
5.1%
22111 1272
 
4.4%
2211 1116
 
3.9%
2111 806
 
2.8%
325412 792
 
2.7%
334413 779
 
2.7%
524126 713
 
2.5%
334516 493
 
1.7%
518210 479
 
1.7%
481111 419
 
1.4%
Other values (168) 20619
71.2%
ValueCountFrequency (%)
42 84
 
0.3%
315 103
 
0.4%
321 82
 
0.3%
325 84
 
0.3%
423 40
 
0.1%
621 76
 
0.3%
2111 806
2.8%
2211 1116
3.9%
3113 101
 
0.3%
3352 92
 
0.3%
ValueCountFrequency (%)
999977 127
0.4%
812331 85
 
0.3%
722513 228
0.8%
722511 67
 
0.2%
721120 40
 
0.1%
721110 155
0.5%
622110 177
0.6%
621511 141
0.5%
621492 65
 
0.2%
621491 83
 
0.3%

Interactions

2023-04-27T15:13:33.421849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:44.280818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:46.721453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:50.064110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:53.395570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:56.362644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:59.446894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:02.356955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:05.144581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:07.671107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:10.256753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:12.939158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:15.454565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:18.199635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:20.706883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:23.348729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-04-27T15:12:45.794585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:49.023155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:52.166083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:55.465847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:58.221113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:01.411689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:04.332810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:06.804809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:09.466267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:11.885721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:14.655467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:17.368300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:19.886368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:22.522952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:25.002384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:28.326769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:32.502916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:35.714410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:45.898058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:49.203616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:52.329886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:55.622538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:58.383344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:01.553973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:04.479862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:06.941820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:09.603039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:12.020407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:14.787549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:17.507333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:20.029425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:22.662166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:25.140135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:28.599207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:32.646694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:35.857338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:46.047946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:49.348967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:52.483921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:55.770146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:58.548039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:01.738368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:04.609223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:07.083479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:09.733848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:12.157488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:14.916021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:17.643901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:20.162613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:22.798953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:25.470052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:29.022382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:32.779674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:36.005420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:46.169773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:49.529711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:52.642975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:55.922997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:58.700933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:01.888372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:04.751259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:07.226542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:09.869050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:12.295227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:15.057916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:17.790661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:20.304323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:22.940501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:25.645094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:29.232523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:32.933413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:36.146798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:46.434127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:49.726033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:52.997849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:56.078225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:59.078361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:02.041418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:04.884030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:07.372069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:10.001110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:12.429540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:15.187025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:17.934332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:20.438698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:23.074848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:25.790274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:29.416323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:33.137266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:36.292754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:46.591278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:49.875720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:53.199769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:56.221704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:12:59.283080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:02.189928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:05.017625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:07.526828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:10.135950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:12.798593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:15.317720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:18.073088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:20.575824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:23.216440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:26.155647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:29.925935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-27T15:13:33.284819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Missing values

2023-04-27T15:13:36.650470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-27T15:13:37.957793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-27T15:13:38.798877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CFOANNEXECDIRREPRICEEXECRANKCO_PER_ROLCONAMECEOANNSALARYBONUSPENSION_CHGTOTAL_SECTOTAL_CURRCHG_CTRL_PYMTAGEEXECRANKANNTDC1ALLOTHTOTALLOTHPDSAL_PCTTOTAL_CURR_PCTTOTAL_SEC_PCTTDC1_PCTCOMMENTGVKEYEXECIDYEARBECAMECEOJOINED_COREJOINLEFTOFCLEFTCORELEFTPCEOPCFOTITLEREASONEXEC_LNAMEEXEC_FNAMEGENDERPAGECUSIPTICKERNAICS
0NaN10NaN1AMERICAN AIRLINES GROUP INCCEO600.000.0NaNNaN600.00NaNNaN1.01013.47187.6340.0NaNNaNNaNNaNNaN10455819921985-03-011973-04-01NaN1998-05-201998-12-31NaNNaNNaNchmn., pres. & CEORETIREDCrandallRobertMALE87.002376R10AAL481111
1NaN00NaN2AMERICAN AIRLINES GROUP INCNaN468.750.0NaNNaN468.75NaNNaNNaN1470.6646.8200.0NaNNaNNaNNaNNaN104536601992NaNNaNNaNNaN2002-04-01NaNNaNNaNvice chairmanRETIREDBakerRobertMALENaN02376R10AAL481111
2NaN00NaN3AMERICAN AIRLINES GROUP INCNaN468.750.0NaNNaN468.75NaNNaNNaN1477.22513.3810.0NaNNaNNaNNaNNaN1045366119921998-05-20NaNNaN2003-04-242003-04-24NaNNaNNaNchairman & CEORESIGNEDCartyDonaldMALE76.002376R10AAL481111
3NaN00NaN4AMERICAN AIRLINES GROUP INCNaN343.750.0NaNNaN343.75NaNNaNNaN718.1044.8690.0NaNNaNNaNNaNNaN104536621992NaNNaNNaNNaN2002-09-17NaNNaNNaNexecutive vp; executive vp-marketing & planning-AmericanRETIREDGunnMichaelMALENaN02376R10AAL481111
4NaN00NaN5AMERICAN AIRLINES GROUP INCNaN343.750.0NaNNaN343.75NaNNaNNaN747.46334.2280.0NaNNaNNaNNaNNaN104536631992NaNNaNNaNNaN1995-01-15NaNNaNNaNsr. v-pRETIREDHopperMaxMALENaN02376R10AAL481111
5NaN10NaN1AMERICAN AIRLINES GROUP INCCEO600.000.0NaNNaN600.00NaN58.01.01003.97473.6310.00.0000.000NaN-0.937NaN10455819931985-03-011973-04-01NaN1998-05-201998-12-31NaNNaNNaNchmn., pres. & CEORETIREDCrandallRobertMALE87.002376R10AAL481111
6NaN00NaN2AMERICAN AIRLINES GROUP INCNaN491.250.0NaNNaN491.25NaNNaN2.01328.59513.2800.04.8004.800NaN-9.660NaN104536601993NaNNaNNaNNaN2002-04-01NaNNaNNaNvice chairmanRETIREDBakerRobertMALENaN02376R10AAL481111
7NaN00NaN3AMERICAN AIRLINES GROUP INCNaN491.250.0NaNNaN491.25NaNNaN3.01328.69613.3810.04.8004.800NaN-10.055NaN1045366119931998-05-20NaNNaN2003-04-242003-04-24NaNNaNNaNchairman & CEORESIGNEDCartyDonaldMALE76.002376R10AAL481111
8NaN00NaN4AMERICAN AIRLINES GROUP INCNaN362.500.0NaNNaN362.50NaNNaN4.0693.60214.0410.05.4555.455NaN-3.412NaN104536621993NaNNaNNaNNaN2002-09-17NaNNaNNaNexecutive vp; executive vp-marketing & planning-AmericanRETIREDGunnMichaelMALENaN02376R10AAL481111
9NaN00NaN5AMERICAN AIRLINES GROUP INCNaN356.250.0NaNNaN356.25NaNNaN5.0691.35932.1080.03.6363.636NaN-7.506NaN104536631993NaNNaNNaNNaN1995-01-15NaNNaNNaNsr. v-pRETIREDHopperMaxMALENaN02376R10AAL481111
CFOANNEXECDIRREPRICEEXECRANKCO_PER_ROLCONAMECEOANNSALARYBONUSPENSION_CHGTOTAL_SECTOTAL_CURRCHG_CTRL_PYMTAGEEXECRANKANNTDC1ALLOTHTOTALLOTHPDSAL_PCTTOTAL_CURR_PCTTOTAL_SEC_PCTTDC1_PCTCOMMENTGVKEYEXECIDYEARBECAMECEOJOINED_COREJOINLEFTOFCLEFTCORELEFTPCEOPCFOTITLEREASONEXEC_LNAMEEXEC_FNAMEGENDERPAGECUSIPTICKERNAICS
28943NaN00NaN31869CBRE GROUP INCNaN366.000585.000NaNNaN951.000NaNNaNNaN1341.7580.0000.00021.06947.028NaNNaNNaN260774305552004NaN2002-01-23NaNNaNNaNNaNNaNNaNExecutive Chairman of Cbre-Asia PacificNaNBlainRobertMALE67.012504L10CBRE531312
28944NaN00NaN31870CBRE GROUP INCNaN458.000663.000NaNNaN1121.000NaNNaNNaN1286.4841.6460.00035.76428.328NaN11.643NaN260774305562004NaN2003-07-23NaNNaN2005-06-30NaNNaNNaNformer president-EMEARESIGNEDFroggattAlanMALENaN12504L10CBRE531312
28945NaN00NaN31871CBRE GROUP INCNaN225.000189.800NaNNaN414.800NaNNaNNaNNaN200.179200.179NaNNaNNaNNaNNaN260774305572004NaN2004-04-26NaNNaNNaNNaNNaNNaNexecutive vp & general counselNaNMidlerLaurenceMALE58.012504L10CBRE531312
28946NaN10NaN31865CBRE GROUP INCCEO608.4631968.900NaNNaN2577.363NaN46.01.03622.3160.0000.00010.63013.741NaN-4.564NaN2607743055220052005-06-01NaNNaN2012-11-30NaNNaNNaNNaNDirectorNaNWhiteW.MALE62.012504L10CBRE531312
28947NaN10NaN31866CBRE GROUP INCNaN416.155882.789NaNNaN1298.944NaN62.02.01327.6470.8570.000-35.976-51.496NaN-65.307NaN2607743055320052001-09-01NaNNaN2005-06-022007-06-02NaNNaNNaNformer CEORETIREDWirtaRayMALE79.012504L10CBRE531312
28948NaN00NaN31867CBRE GROUP INCNaN450.000592.200NaNNaN1042.200NaN50.04.01555.0000.0000.0000.0008.337NaN13.372NaN260774215622005NaN2002-06-13NaNNaN2008-11-30NaNNaNNaNformer senior executive vp & chief finance officerRESIGNEDKayKennethMALE67.012504L10CBRE531312
28949NaN00NaN31868CBRE GROUP INCNaN500.000762.000NaNNaN1262.000NaN49.03.01929.4980.8570.00011.1114.852NaNNaNNaN260774305542005NaNNaNNaNNaNNaNNaNNaNNaNGlobal Group President of GeographiesNaNFrese, Jr.CalvinMALE65.012504L10CBRE531312
28950NaN00NaN31869CBRE GROUP INCNaN383.437499.896NaNNaN883.333NaN50.05.01541.6150.0000.0004.764-7.115NaN14.895NaN260774305552005NaN2002-01-23NaNNaNNaNNaNNaNNaNExecutive Chairman of Cbre-Asia PacificNaNBlainRobertMALE67.012504L10CBRE531312
28951NaN00NaN31870CBRE GROUP INCNaN389.250472.290NaNNaN861.540NaNNaN6.0863.0331.4930.000-15.011-23.145NaN-32.915NaN260774305562005NaN2003-07-23NaNNaN2005-06-30NaNNaNNaNformer president-EMEARESIGNEDFroggattAlanMALENaN12504L10CBRE531312
28952NaN00NaN31871CBRE GROUP INCNaN325.000214.700NaNNaN539.700NaN41.07.0962.20812.26811.70044.44430.111NaNNaNNaN260774305572005NaN2004-04-26NaNNaNNaNNaNNaNNaNexecutive vp & general counselNaNMidlerLaurenceMALE58.012504L10CBRE531312